Differential Gene Expression Analysis - ALL samples
oi889 - RN25025
Irene Rodriguez-Hernandez
April 2025
Experimental design and pre-processing
Data were processed using nf-core rnaseq pipeline revision 3.14.0 using RSEM/STAR for abundance estimation against Homo Sapiens GRCh38 release-86.
| sample_id | sample_name | condition | condition_name | plate_id |
|---|---|---|---|---|
| ING7705A1 | C_1 | C | control | 1 |
| ING7705A2 | DN_1 | DN | SOX17neg_FOXC1neg | 1 |
| ING7705A3 | DP_1 | DP | SOX17pos_FOXC1pos | 1 |
| ING7705A4 | A_1 | A | SOX17neg_FOXC1pos | 1 |
| ING7705A5 | S17_1 | S17 | SOX17pos_FOXC1neg | 1 |
| ING7705A6 | C_2 | C | control | 2 |
| ING7705A7 | DN_2 | DN | SOX17neg_FOXC1neg | 2 |
| ING7705A8 | DP_2 | DP | SOX17pos_FOXC1pos | 2 |
| ING7705A9 | A_2 | A | SOX17neg_FOXC1pos | 2 |
| ING7705A10 | S17_2 | S17 | SOX17pos_FOXC1neg | 2 |
| ING7705A11 | C_3 | C | control | 3 |
| ING7705A12 | DN_3 | DN | SOX17neg_FOXC1neg | 3 |
| ING7705A13 | DP_3 | DP | SOX17pos_FOXC1pos | 3 |
| ING7705A14 | A_3 | A | SOX17neg_FOXC1pos | 3 |
| ING7705A15 | S17_3 | S17 | SOX17pos_FOXC1neg | 3 |
QC
Counts (raw)
Raw read counts for each gene per sample
Counts (normalised)
Normalized counts using size factors.
Expressed genes
Expressed genes are defined as those that have count > 0 in at least 1 sample.
Exploratory analysis
Sample distance
Heatmap of samples distances to assess overall similarities and dissimilarities between samples. Sample similarity is assessed using a Poisson dissimilarity metric, which is fairly robust to differences in library sizes between samples.
PCA
Principal Component Analysis (PCA) based on the variance stabilised abundance estimates. It uses all available genes. The majority of the variance in the dataset is shown in the two PC and associated with a time effect.
Screeplot
Bi-plot condition
Bi-plot replicate
Top 10 Loadings
Eigen-correlation
Heatmaps (most variable)
Heatmaps of the top most variable genes across all samples. The top 500 genes based on a coefficient of variation are displayed (sd/mean). Note that don’t necessarily relate to genes changing significantly between replicate conditions. These visualisations are carried out blind to the experimental design. We’d expect samples from the same experimental group to cluster together.
Differential Gene Expression Analysis - Pairwise Comparisons
\[ design = plate_id + condition \]
Pairwise differential expression between condition groups was assessed using a DESeq2’s Wald test. Significance was assessed based on an independent hypothesis weighting (IHW) value of < 0.05, together with a minimum fold-change of > 0 and a minimum baseMean (i.e. mean abundance across all samples) of > 5. Note that log2 fold changes were shrunk using the “ashr” method prior to filtering.
Thresholds applied:
- padj < 0.05
- FC > 0
- baseMean > 5
| comparison | up | down | total |
|---|---|---|---|
| DN_vs_C | 2765 | 2687 | 5452 |
| DP_vs_C | 4644 | 4419 | 9063 |
| A_vs_C | 4499 | 4229 | 8728 |
| S17_vs_C | 4599 | 4216 | 8815 |
| DP_vs_DN | 3573 | 3529 | 7102 |
| A_vs_DN | 3392 | 3346 | 6738 |
| S17_vs_DN | 3196 | 2662 | 5858 |
| DP_vs_A | 1864 | 1836 | 3700 |
| DP_vs_S17 | 1448 | 1833 | 3281 |
| S17_vs_A | 2572 | 2113 | 4685 |
MA plots
DN_vs_C
DP_vs_C
A_vs_C
S17_vs_C
DP_vs_DN
A_vs_DN
S17_vs_DN
DP_vs_A
DP_vs_S17
S17_vs_A
Gene list overlaps
Assess the overlap of differential genes between the various comparisons. Note that this is agnostic of direction of change.
Differential Heatmaps (top genes)
Heatmaps of the top 30 most up-regulated and top 30 most down-regulated differentially expressed genes per comparison. If fewer than 30 genes are called differential in either direction, then only those are displayed. If there are no differential genes then the heatmap is skipped.
DN_vs_C
DP_vs_C
A_vs_C
S17_vs_C
DP_vs_DN
A_vs_DN
S17_vs_DN
DP_vs_A
DP_vs_S17
S17_vs_A
Differential Heatmaps (all differential genes)
Heatmap derived from a combined view of all differentially expressed genes from all available tests (13680 genes).
sessionInfo
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] grid stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] writexl_1.5.2 openxlsx_4.2.8
## [3] kableExtra_1.4.0 clusterProfiler_4.14.6
## [5] PoiClaClu_1.0.2.1 PCAtools_2.18.0
## [7] ggrepel_0.9.6 ashr_2.2-63
## [9] IHW_1.34.0 edgeR_4.4.2
## [11] limma_3.62.2 DESeq2_1.46.0
## [13] SummarizedExperiment_1.36.0 MatrixGenerics_1.18.1
## [15] matrixStats_1.5.0 tximport_1.34.0
## [17] org.Hs.eg.db_3.20.0 UpSetR_1.4.0
## [19] GenomicFeatures_1.58.0 AnnotationDbi_1.68.0
## [21] Biobase_2.66.0 BiocParallel_1.40.0
## [23] scales_1.3.0 reshape2_1.4.4
## [25] viridis_0.6.5 viridisLite_0.4.2
## [27] pheatmap_1.0.12 circlize_0.4.16
## [29] ComplexHeatmap_2.22.0 RColorBrewer_1.1-3
## [31] plyranges_1.26.0 GenomicRanges_1.58.0
## [33] GenomeInfoDb_1.42.3 IRanges_2.40.1
## [35] S4Vectors_0.44.0 BiocGenerics_0.52.0
## [37] lubridate_1.9.4 forcats_1.0.0
## [39] stringr_1.5.1 dplyr_1.1.4
## [41] purrr_1.0.4 readr_2.1.5
## [43] tidyr_1.3.1 tibble_3.2.1
## [45] ggplot2_3.5.1 tidyverse_2.0.0
## [47] here_1.0.1
##
## loaded via a namespace (and not attached):
## [1] splines_4.4.1 BiocIO_1.16.0
## [3] ggplotify_0.1.2 bitops_1.0-9
## [5] R.oo_1.27.0 lpsymphony_1.34.0
## [7] XML_3.99-0.18 lifecycle_1.0.4
## [9] mixsqp_0.3-54 doParallel_1.0.17
## [11] rprojroot_2.0.4 vroom_1.6.5
## [13] lattice_0.22-6 magrittr_2.0.3
## [15] sass_0.4.9 rmarkdown_2.29
## [17] jquerylib_0.1.4 yaml_2.3.10
## [19] ggtangle_0.0.6 zip_2.3.2
## [21] cowplot_1.1.3 DBI_1.2.3
## [23] abind_1.4-8 zlibbioc_1.52.0
## [25] R.utils_2.13.0 RCurl_1.98-1.17
## [27] yulab.utils_0.2.0 GenomeInfoDbData_1.2.13
## [29] enrichplot_1.26.6 irlba_2.3.5.1
## [31] tidytree_0.4.6 dqrng_0.4.1
## [33] svglite_2.1.3 DelayedMatrixStats_1.28.1
## [35] codetools_0.2-20 DelayedArray_0.32.0
## [37] xml2_1.3.8 DOSE_4.0.1
## [39] tidyselect_1.2.1 shape_1.4.6.1
## [41] aplot_0.2.5 farver_2.1.2
## [43] UCSC.utils_1.2.0 ScaledMatrix_1.14.0
## [45] GenomicAlignments_1.42.0 jsonlite_2.0.0
## [47] GetoptLong_1.0.5 iterators_1.0.14
## [49] systemfonts_1.2.1 foreach_1.5.2
## [51] tools_4.4.1 treeio_1.30.0
## [53] Rcpp_1.0.14 glue_1.8.0
## [55] gridExtra_2.3 SparseArray_1.6.2
## [57] xfun_0.51 qvalue_2.38.0
## [59] withr_3.0.2 fastmap_1.2.0
## [61] digest_0.6.37 rsvd_1.0.5
## [63] truncnorm_1.0-9 gridGraphics_0.5-1
## [65] timechange_0.3.0 R6_2.6.1
## [67] colorspace_2.1-1 GO.db_3.20.0
## [69] RSQLite_2.3.9 R.methodsS3_1.8.2
## [71] generics_0.1.3 data.table_1.17.0
## [73] rtracklayer_1.66.0 httr_1.4.7
## [75] S4Arrays_1.6.0 pkgconfig_2.0.3
## [77] gtable_0.3.6 blob_1.2.4
## [79] XVector_0.46.0 htmltools_0.5.8.1
## [81] fgsea_1.32.4 clue_0.3-66
## [83] png_0.1-8 ggfun_0.1.8
## [85] knitr_1.50 rstudioapi_0.17.1
## [87] tzdb_0.5.0 rjson_0.2.23
## [89] nlme_3.1-168 curl_6.2.2
## [91] cachem_1.1.0 GlobalOptions_0.1.2
## [93] parallel_4.4.1 restfulr_0.0.15
## [95] pillar_1.10.1 vctrs_0.6.5
## [97] slam_0.1-55 BiocSingular_1.22.0
## [99] beachmat_2.22.0 cluster_2.1.8.1
## [101] evaluate_1.0.3 invgamma_1.1
## [103] cli_3.6.4 locfit_1.5-9.12
## [105] compiler_4.4.1 Rsamtools_2.22.0
## [107] rlang_1.1.5 crayon_1.5.3
## [109] SQUAREM_2021.1 labeling_0.4.3
## [111] fdrtool_1.2.18 plyr_1.8.9
## [113] fs_1.6.5 stringi_1.8.7
## [115] munsell_0.5.1 Biostrings_2.74.1
## [117] lazyeval_0.2.2 GOSemSim_2.32.0
## [119] Matrix_1.7-3 patchwork_1.3.0
## [121] hms_1.1.3 sparseMatrixStats_1.18.0
## [123] bit64_4.6.0-1 KEGGREST_1.46.0
## [125] statmod_1.5.0 igraph_2.1.4
## [127] memoise_2.0.1 bslib_0.9.0
## [129] ggtree_3.14.0 fastmatch_1.1-6
## [131] bit_4.6.0 gson_0.1.0
## [133] ape_5.8-1